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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include "sparse_112_dot_product.h"
#include <vespa/eval/eval/fast_value.hpp>
#include <vespa/vespalib/util/typify.h>
#include <vespa/vespalib/util/require.h>
#include <vespa/eval/eval/visit_stuff.h>
#include <algorithm>
namespace vespalib::eval {
using namespace tensor_function;
using namespace operation;
using namespace instruction;
namespace {
template <typename T, size_t N>
ConstArrayRef<T> as_car(std::array<T, N> &array) noexcept {
return {array.data(), array.size()};
}
template <typename T, size_t N>
ConstArrayRef<const T *> as_ccar(std::array<T *, N> &array) noexcept {
return {array.data(), array.size()};
}
template <typename T>
ConstArrayRef<T> as_car(T &value) noexcept {
return {&value, 1};
}
constexpr std::array<size_t, 2> both_dims = { 0, 1 };
template <typename CT>
double my_sparse_112_dot_product_fallback(const Value::Index &a_idx, const Value::Index &b_idx, const Value::Index &c_idx,
const CT *a_cells, const CT *b_cells, const CT *c_cells) __attribute__((noinline));
template <typename CT>
double my_sparse_112_dot_product_fallback(const Value::Index &a_idx, const Value::Index &b_idx, const Value::Index &c_idx,
const CT *a_cells, const CT *b_cells, const CT *c_cells)
{
double result = 0.0;
size_t a_space = 0;
size_t b_space = 0;
size_t c_space = 0;
std::array<string_id, 2> c_addr;
std::array<string_id*, 2> c_addr_ref = {&c_addr[0], &c_addr[1]};
auto outer = a_idx.create_view({});
auto inner = b_idx.create_view({});
auto model = c_idx.create_view({&both_dims[0], 2});
outer->lookup({});
while (outer->next_result(as_car(c_addr_ref[0]), a_space)) {
inner->lookup({});
while (inner->next_result(as_car(c_addr_ref[1]), b_space)) {
model->lookup(as_ccar(c_addr_ref));
if (model->next_result({}, c_space)) {
result += (a_cells[a_space] * b_cells[b_space] * c_cells[c_space]);
}
}
}
return result;
}
template <typename CT>
double my_fast_sparse_112_dot_product(const FastAddrMap *a_map, const FastAddrMap *b_map, const FastAddrMap *c_map,
const CT *a_cells, const CT *b_cells, const CT *c_cells) __attribute__((noinline));
template <typename CT>
double my_fast_sparse_112_dot_product(const FastAddrMap *a_map, const FastAddrMap *b_map, const FastAddrMap *c_map,
const CT *a_cells, const CT *b_cells, const CT *c_cells)
{
double result = 0.0;
std::array<string_id, 2> c_addr;
const auto &a_labels = a_map->labels();
for (size_t a_space = 0; a_space < a_labels.size(); ++a_space) {
if (a_cells[a_space] != 0.0) { // handle pseudo-sparse input
c_addr[0] = a_labels[a_space];
const auto &b_labels = b_map->labels();
for (size_t b_space = 0; b_space < b_labels.size(); ++b_space) {
if (b_cells[b_space] != 0.0) { // handle pseudo-sparse input
c_addr[1] = b_labels[b_space];
auto c_space = c_map->lookup(as_car(c_addr));
if (c_space != FastAddrMap::npos()) {
result += (a_cells[a_space] * b_cells[b_space] * c_cells[c_space]);
}
}
}
}
}
return result;
}
template <typename CT>
void my_sparse_112_dot_product_op(InterpretedFunction::State &state, uint64_t) {
const auto &a_idx = state.peek(2).index();
const auto &b_idx = state.peek(1).index();
const auto &c_idx = state.peek(0).index();
const CT *a_cells = state.peek(2).cells().unsafe_typify<CT>().cbegin();
const CT *b_cells = state.peek(1).cells().unsafe_typify<CT>().cbegin();
const CT *c_cells = state.peek(0).cells().unsafe_typify<CT>().cbegin();
double result = __builtin_expect(are_fast(a_idx, b_idx, c_idx), true)
? my_fast_sparse_112_dot_product<CT>(&as_fast(a_idx).map, &as_fast(b_idx).map, &as_fast(c_idx).map,
a_cells, b_cells, c_cells)
: my_sparse_112_dot_product_fallback<CT>(a_idx, b_idx, c_idx, a_cells, b_cells, c_cells);
state.pop_pop_pop_push(state.stash.create<DoubleValue>(result));
}
struct MyGetFun {
template <typename CT>
static auto invoke() { return my_sparse_112_dot_product_op<CT>; }
};
using MyTypify = TypifyValue<TypifyCellType>;
// Try to collect input nodes and organize them into a dot product
// between (n sparse non-overlapping single-dimension tensors) and (a
// sparse n-dimensional tensor) all having the same cell type.
struct InputState {
std::vector<const TensorFunction *> single;
const TensorFunction *multi = nullptr;
bool collision = false;
void collect(const TensorFunction &node) {
const auto &type = node.result_type();
if (type.is_sparse()) {
if (type.dimensions().size() == 1) {
single.push_back(&node);
} else {
if (multi) {
collision = true;
} else {
multi = &node;
}
}
}
}
void finalize() {
std::sort(single.begin(), single.end(), [](const auto *a, const auto *b)
{ return (a->result_type().dimensions()[0].name < b->result_type().dimensions()[0].name); });
}
bool verify(size_t n) const {
if (collision || (single.size() != n) || (multi == nullptr) || (multi->result_type().dimensions().size() != n)) {
return false;
}
const auto &multi_type = multi->result_type();
for (size_t i = 0; i < n; ++i) {
const auto &single_type = single[i]->result_type();
if ((single_type.cell_type() != multi_type.cell_type()) ||
(single_type.dimensions()[0].name != multi_type.dimensions()[i].name))
{
return false;
}
}
return true;
}
};
// Try to find inputs that form a 112 dot product.
struct FindInputs {
const TensorFunction *a = nullptr;
const TensorFunction *b = nullptr;
const TensorFunction *c = nullptr;
bool try_match(const TensorFunction &one, const TensorFunction &two) {
auto join = as<Join>(two);
if (join && (join->function() == Mul::f)) {
InputState state;
state.collect(one);
state.collect(join->lhs());
state.collect(join->rhs());
state.finalize();
if (state.verify(2)) {
a = state.single[0];
b = state.single[1];
c = state.multi;
return true;
}
}
return false;
}
};
} // namespace <unnamed>
Sparse112DotProduct::Sparse112DotProduct(const TensorFunction &a_in,
const TensorFunction &b_in,
const TensorFunction &c_in)
: tensor_function::Node(DoubleValue::shared_type()),
_a(a_in),
_b(b_in),
_c(c_in)
{
}
InterpretedFunction::Instruction
Sparse112DotProduct::compile_self(const ValueBuilderFactory &, Stash &) const
{
REQUIRE_EQ(_a.get().result_type().cell_type(), _b.get().result_type().cell_type());
REQUIRE_EQ(_a.get().result_type().cell_type(), _c.get().result_type().cell_type());
auto op = typify_invoke<1,MyTypify,MyGetFun>(_a.get().result_type().cell_type());
return InterpretedFunction::Instruction(op);
}
void
Sparse112DotProduct::push_children(std::vector<Child::CREF> &children) const
{
children.emplace_back(_a);
children.emplace_back(_b);
children.emplace_back(_c);
}
void
Sparse112DotProduct::visit_children(vespalib::ObjectVisitor &visitor) const
{
::visit(visitor, "a", _a.get());
::visit(visitor, "b", _b.get());
::visit(visitor, "c", _c.get());
}
const TensorFunction &
Sparse112DotProduct::optimize(const TensorFunction &expr, Stash &stash)
{
auto reduce = as<Reduce>(expr);
if (reduce && (reduce->aggr() == Aggr::SUM) && expr.result_type().is_double()) {
auto join = as<Join>(reduce->child());
if (join && (join->function() == Mul::f)) {
FindInputs inputs;
if (inputs.try_match(join->lhs(), join->rhs()) ||
inputs.try_match(join->rhs(), join->lhs()))
{
return stash.create<Sparse112DotProduct>(*inputs.a, *inputs.b, *inputs.c);
}
}
}
return expr;
}
} // namespace
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